Proposed Planning Process for the Emission Inventory - 6-1-12 - PDF

582-11-11219 Task 3.2 Proposed Planning Process for the Emission Inventory Technical Proposal June 1st, 2012 Prepared by: Alamo Area Council of Governments Prepared in Cooperation with the Texas Commission on Environmental Quality The preparation of this report was financed through grants from the State of Texas through the Texas Commission on Environmental Quality i Title: Proposed Planning Process for the Report Date: June 1st, 2012 Emission Inventory Authors: AACOG Natural Resources/ Type of Report: Technical Report Transportation Department Period Covered: 2006 Performing Organization Name & Address: Alamo Area Council of Governments 8700 Tesoro Drive Suite 700 San Antonio, Texas 78217 Sponsoring Agency: Prepared In Cooperation With The Texas Commission on Environmental Quality The preparation of this report was financed through grants from the State of Texas through the Texas Commission on Environmental Quality Abstract: The Clean Air Act is the comprehensive federal law that regulates airborne emissions across the United States This law authorizes the U.S Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards (NAAQS) to protect public health and the environment San Antonio is currently in attainment of the “criteria” pollutants according to the NAAQS However, high concentrations of ground level ozone, one of the “criteria” pollutants, are measured periodically at local monitors Ozone forms in the lower atmosphere when organic compounds (VOC) and nitrogen compounds (NOX) react in the presence of sunlight Currently, the ozone primary standard, which is designed to protect human health, is set at 75 parts per billion (ppb) Local and state air quality planners need to have an accurate account of emissions and their sources in the region to identify trends, analyze the effects of regulatory actions, and simulate air quality and atmospheric processes through computer dispersion modeling The objective of the proposal is to provide a review and update of the emission inventory for 2011 including the following categories: municipal and small airports, recreational marine vessels, commercial lawn and garden equipment, and agricultural pesticide applications Emission estimates from these categories have been over estimated or under estimated when traditional methodologies were used By using local data and surveys, improvements in the amount and spatial allocation of emissions are achieved When improved emission estimates are developed, control strategy modeling and SIP development is improved The proposed updates will include sources in the San AntonioNew Braunfels MSA, consisting of eight counties located in South Central Texas and part of the Hill Country The results could be considered for inclusion in the regional photochemical model Related Reports: 2005 Emission Inventory for the Alamo Area Council of Governments Region Distribution Statement: Alamo Area Council of Governments, Natural Resources/Transportation Department ii Permanent File: Alamo Area Council of Governments, Natural Resources/Transportation Department EXECUTIVE SUMMARY The Clean Air Act is the comprehensive federal law that regulates airborne emissions across the United States.1 This law authorizes the U.S Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards (NAAQS) to protect public health and the environment Of the many air pollutants commonly found throughout the country, EPA has recognized six pollutants named “criteria” pollutants that can injure health, harm the environment, or cause property damage Air quality monitors measure concentrations of these pollutants throughout the country As of the end of 2011, San Antonio/Bexar County is in attainment of the “criteria” pollutants based on current NAAQS thresholds However, high concentrations of ground level ozone, one of the “criteria” pollutants, are measured periodically at monitors located in northern Bexar County, placing the area at risk for failing the NAAQS in the future Ozone forms when organic compounds (VOC) and nitrogen compounds (NOX) react in the presence of sunlight.2 Currently, the ozone primary standard, which is designed to protect human health, is set at 75 parts per billion (ppb) The secondary standard, which is designed to protect the environment, is in the same form and concentration as the primary standard One of the air quality management tools local and state planners use to identify trends, analyze the effect of regulatory actions, and run photochemical models is an accurate account of emissions and their sources The objective of the proposal is to provide a review and update of the emission inventory in 2011 for the San Antonio region including the following categories: small airports, recreational marine vessels, commercial lawn and garden equipment, and agricultural pesticide applications Emission estimates from these categories have been over estimated or under estimated when traditional methodologies were used By using local data and surveys, improvements in the amount and spatial allocation of emissions are achieved When improved emission estimates are developed, control strategy modeling and SIP development is improved The proposed updates will include sources in the San Antonio-New Braunfels MSA, consisting of eight counties located in South Central Texas and part of the Hill Country The results could be considered for inclusion in the regional photochemical model US Congress, 1990 “Clean Air Act” Available online: http://www.epa.gov/air/caa/ Accessed 07/19/10 EPA, Sept 23, 2011, “Ground-level Ozone” Available online: http://www.epa.gov/air/ozonepollution/ Accessed 10/31/11 iii TABLE OF CONTENTS EXECUTIVE SUMMARY iii  TABLE OF CONTENTS iv  LIST OF FIGURES vi  LIST OF TABLES vii  LIST OF EQUATIONS viii  1.  INTRODUCTION 1-1  1.1.  Background 1-1  1.2.  Objectives and Approach 1-1  1.3.  Inventory Pollutants 1-2  1.4.  Geographic Area 1-2  1.5.  Modeling Domain Parameters 1-2  1.6.  Data Sources 1-2  1.7.  Refined Categories 1-4  2.  MUNICIPAL AND SMALL AIRPORTS 2-1  2.1.  Aircraft at Municipal and Small Airports 2-1  2.1.1.  Collect Activity Data for General Aviation Aircraft 2-1  2.1.2.  Collect Data on Military Aircraft 2-2  2.1.3.  Perform Survey of Municipal and Small Airports 2-2  2.1.4.  Calculate Emissions using EDMS Model 2-2  2.1.5.  Spatially Distribute Municipal and Small Airport Aircraft Emissions 2-2  2.2.  Evaporative Emissions 2-3  2.2.1.  Refueling and Spillage Loss 2-4  2.2.2.  Diurnal Losses 2-5  2.2.3.  Pre-flight Safety Check 2-5  2.2.4.  Spatially Allocate Evaporative Emissions 2-5  2.3.  Non-road Equipment at Municipal and Small Airports 2-6  2.3.1.  Conduct a Survey of Equipment at Municipal and Small Airports 2-6  2.3.2.  Estimate Equipment Population and Usage for the Municipal and Small Airports that did not Respond to the Survey 2-6  2.3.3.  Estimate VOC and NOX Emissions from Non-road Equipment 2-7  2.3.4.  Weekday Adjustment for Equipment Use 2-7  2.3.5.  Spatial Distribution of Municipal and Small Airport Non-Road Equipment Emissions 2-7  3.  Recreational Marine Vessels 3-1  3.1.  Methodology 3-1  3.2.  Lakes and Boating Ramps in the San Antonio MSA 3-1  3.3.  Survey of Recreational Marine Vessels 3-2  3-4  3.4.  Estimate VOC and NOX annual emissions 3.5.  Temporal Allocation 3-4  3.6.  Spatial Allocation of Emissions 3-4  4.  Commercial Lawn and Garden Equipment 4-1  4.1.  Methodology for Commercial Lawn and Garden Equipment 4-1  4.2.  Survey of Commercial Lawn and Garden Equipment Activity 4-1  4.3.  Determine Equipment Population for Facilities that are Missing Local Data 4-2  4.3.1.  Golf Courses 4-2  4.3.2.  Universities/Colleges 4-5  4.3.1.  Public Schools 4-6  4.3.2.  Commercial Lawn and Garden and Land Clearing Companies 4-7  iv 4.3.3.  Non-Military Government Facilities, Parks, and Hospitals 4.4.  Conduct a Second Survey of Commercial Lawn and Garden Equipment 4.5.  Estimate Ozone Precursors Emissions 4.6.  Temporal Allocation 4.7.  Spatial Distribution 5.  Agricultural Pesticide Applications 5.1.  Determine Pesticides Used on Area Crops and Application Timetable 5.2.  Calculate Emission Factors for Each Pesticide 5.3.  Estimate Precursor Emissions from Pesticides 5.4.  Spatial Allocation of Emissions v 4-7  4-8  4-8  4-10  4-10  5-1  5-1  5-4  5-6  5-6  LIST OF FIGURES Figure 1-1: San Antonio-New Braunfels MSA and 2008 Population Estimates 1-3  Figure 2-1: Location of Municipal and Small Airports in the San Antonio-New Braunfels MSA, 2011 2-3  Figure 3-1: Lakes Used by Recreational Marine Vessels in the San Antonio-New Braunfels MSA 3-5  Figure 4-1: Location of Golf Courses in the San Antonio-New Braunfels MSA 4-10  Figure 4-2: Locations of Universities and Colleges in the San Antonio-New Braunfels MSA 4-11  Figure 4-3: Locations of Public Schools in the San Antonio-New Braunfels MSA 4-12  Figure 5-1: Acres of Corn, Peanuts, Wheat, Sorghum, and Cotton for each 4km Photochemical Modeling Grid 5-8  vi LIST OF TABLES Table 1-1: Contribution of Emissions for Each Proposed Refined Category in the 2005 San Antonio in Tons per Day and as a Percentage of Total Anthropogenic Emissions – New Braunfels MSA Emission Inventory, tons/day 1-4  Table 2-1: Operations and Based Aircraft at Small Airports, 2006 2-1  Table 2-2: Operations and Based Aircraft at Twin Oaks Airport, 2006 2-1  Table 2-3: Annual TGO Military Operations at Small Airports by Aircraft Type, 2006 2-2  Table 3-1: Primary Lakes in the San Antonio-New Braunfels MSA where Recreational Marine Vessels Operate 3-2  Table 3-2: Boat Ramps in the San Antonio-New Braunfels MSA 3-3  Table 4-1: Number of Acres for Golf Courses by County, 2008 4-5  Table 4-2: Number of Acres for University and Colleges by County, 2008 4-5  Table 4-3: Allocation of Public Schools by County, 2009 4-6  Table 4-4: Commercial Lawn and Garden Companies in the San Antonio-New Braunfels MSA, 2008 4-7  Table 4-5: Comparison of Pervious Surveys Equipment Population Estimations, NONROAD Model 2008a, and TexN Model Existing Estimates, San Antonio-New Braunfels MSA 4-9  Table 5-1: Acres Harvested by Crop Type for Each County in the San Antonio-New Braunfels MSA, 2006 5-2  Table 5-2: Types of Pesticides Used and Seasonal Adjustment 5-2  Table 5-3: Pesticide Application, Usage Rate, and Number of Applications 5-4  vii LIST OF EQUATIONS Equation 2-1, Number of operations for each aircraft type at each airport 2-2  Equation 2-2, Emissions from AvGAS refueling at airports with survey data 2-4  Equation 2-3, Emissions from AvGAS refueling at airports without survey data 2-4  Equation 2-4, Emissions from aircraft diurnal losses 2-5  Equation 2-5, Pre-flight safety check emissions 2-5  Equation 2-6, Populations of equipment used at small airports in the San Antonio-New Braunfels MSA 2-7  Equation 2-7, Ozone Season Daily Equipment Emissions for Municipal and Small Airport 2-7  Equation 3-1, Ozone season daily emissions for recreational marine vessels 3-4  Equation 4-1, Equipment to acre ratio for golf courses 4-2  Equation 4-2, Estimated golf course equipment population by equipment type 4-2  Equation 4-3, Equipment to acre ratio for universities and colleges 4-5  Equation 4-4, Estimated universities and colleges equipment populations by equipment type 4-5  Equation 4-5, Equipment to number of schools ratio for public school districts 4-6  Equation 4-6, Estimated equipment population by equipment type for public schools that did not respond to the survey 4-6  Equation 4-7, Equipment to number of commercial companies ratio 4-7  Equation 4-8, Estimated commercial companies’ equipment population by equipment type 4-7  Equation 4-9, Ozone season daily emissions for commercial lawn and garden equipment 4-8  Equation 5-1, Active ingredient emission factor per acre for pesticides 5-4  Equation 5-2, Inert ingredient emission factor for pesticides 5-6  Equation 5-3, Total emission factor for each pesticide 5-6  Equation 5-4, Ozone season daily VOC emissions for pesticides use 5-6  viii INTRODUCTION 1.1 Background The Clean Air Act is the comprehensive federal law that regulates airborne emissions across the United States.3 This law authorizes the U.S Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards (NAAQS) to protect public health and the environment Of the many air pollutants commonly found throughout the country, EPA has recognized six pollutants, often referred to as “criteria” pollutants that can injure health, harm the environment, or cause property damage Air quality monitors measure concentrations of these pollutants throughout the country San Antonio is currently in attainment of all “criteria” pollutants in accordance with thresholds established for the NAAQS However, there are concerns over the high concentrations of ground level ozone, one of the “criteria” pollutants, which local monitors are recording Ozone forms in the lower atmosphere when organic compounds (VOC) and nitrogen compounds (NOX) react in the presence of sunlight.4 According to the EPA, “the health effects associated with ozone exposure include respiratory health problems ranging from decreased lung function and aggravated asthma to increased emergency department visits, hospital admissions and premature death The environmental effects associated with seasonal exposure to ground-level ozone include adverse effects on sensitive vegetation, forests, and ecosystems.”5 Currently, the ozone primary standard, which is designed to protect human health, is set at 75 parts per billion (ppb) The secondary standard, which is designed to protect the environment, is in the same form and concentration as the primary standard Developing an accurate account of emissions and their sources in the region is a basic step in the air quality management process and allows local and state planners to identify appropriate controls and analyze their effectiveness The results for the emission inventory could be considered for inclusion in the regional photochemical model 1.2 Objectives and Approach The objective of the proposal is to provide a review of and a proposal to update the 2011 emission inventory input data using local data and surveys By using local data and surveys, accuracy of estimates and spatial allocation of emission sources are improved The proposal follows the four steps listed below Review the National Emissions Inventories (NEI) provided by the TCEQ and compares those estimates to AACOG’s emission inventory Identify any significant source categories that are under or over estimated or where additional or more detailed emissions inventory inputs at a sub-county level can be provided Identify emission sources and prepare a plan to carry out “bottom-up” research that will provide improved emissions inventory inputs Develop a plan to generate raw local inputs such as population figures, local activity profiles, spatial surrogates, temporal profiles, or TexN Model files, etc Provide all electronic data sets to TCEQ, including but not limited to: TexN files; TexN run specifications; EPS3 input files; and EPS3 spatial and temporal surrogates US Congress, 1990 “Clean Air Act” Available online: http://www.epa.gov/air/caa/ Accessed 07/19/10 EPA, Sept 23, 2011, “Ground-level Ozone” Available online: http://www.epa.gov/air/ozonepollution/ Accessed 10/31/11 EPA, September 16, 2009 “Fact Sheet: EPA to Reconsider Ozone Pollution Standards”, p Available online: http://www.epa.gov/air/ozonepollution/pdfs/O3_Reconsideration_FACT%20SHEET_091609.pdf Accessed 06/28/10 1-1 The focus of these improvements is not the end-product generation of emissions estimates in units of tons per day, but rather the raw local inputs such as population figures, local activity profiles, spatial surrogates, temporal profiles, and other input data All proposed survey work in this plan is accompanied by a survey design describing the population, the information to be collected from the population, a description of how AACOG intends to collect a sample, the type of sample to be drawn from the population, and the desired margin of error 1.3 Inventory Pollutants Ozone is a secondary pollutant because it forms from the chemical reaction between other pollutants, namely: • Nitrogen Oxides (NOX) • Volatile Organic Compounds (VOC) The photochemical modeling that is conducted to determine a region’s ability to comply with the NAAQS depends on accurate input, such as identifying and quantifying emission rates from these pollutants 1.4 Geographic Area The proposed updates to the emission inventory will include all sources in the San Antonio-New Braunfels MSA, consisting of eight counties located in South Central Texas and part of the Hill Country These counties are: Atascosa, Bandera, Bexar, Comal, Guadalupe, Kendall, Medina, and Wilson counties (figure 1-1) 1.5 Modeling Domain Parameters Development of input files and/or spatial surrogates for photochemical model emission processing shall be based on a grid system consistent with EPA’s Regional Planning Organizations (RPO) Lambert Conformal Conic map projection with the following parameters: • • • • • First True Latitude (Alpha): Second True Latitude (Beta): Central Longitude (Gamma): Projection Origin: Spheroid: Perfect Sphere, Radius: 33°N 45°N 97°W (97°W, 40°N) 6,370 km All future TCEQ photochemical model emissions processing work shall be based on this grid system 1.6 Data Sources Specific emissions input data will be calculated by AACOG based on protocols provided by EPA and TCEQ Emission calculations will be based on the local activity data collected through surveys and TexN Model6 Other data sources include County Business Patterns,7 Federal Aviation Administration,8 Airport IQ Data Center9, Texas Agricultural Statistics10, and Texas Assessment and Standards Division Office of Transportation and Air Quality U.S Environmental Protection Agency, July 2009 “NONROAD2008a Model” Available online: http://www.epa.gov/otaq/nonrdmdl.htm Accessed 06/13/11 U.S Census Bureau, July 2009 “2007 County Business Patterns (NAICS)” Available online: http://www.census.gov/econ/cbp/index.html Accessed 07/21/11 Federal Aviation Administration, June 2, 2005 “Air Quality Procedures for Civilian Airports and Air Force Bases, Appendix D: Aircraft Emission Methodology” Office of Environment and Energy Washington, DC p D-5 Available online: http://www.faa.gov/regulations_policies/policy_guidance/envir_policy/airquality_handbook/media/App_D PDF Accessed 08/05/11 GCR & Associates, Inc., 2005 Airport IQ Data Center Available online: http://www.gcr1.com/5010WEB/ 1-2 Where, UPOPAB = Population of equipment type A for universities and colleges B ACRESB = Number of acres for universities and colleges B (from aerial photography and appraisal districts) RATIOA = Ratio of equipment type A per acre (from equation 4-3) 4.3.1 Public Schools A similar method will be used to calculate equipment for independent school districts, but it will be based on the number of public schools provided in Table 4-3 Emissions will be calculated for school districts instead of individual schools because school districts often have one central maintenance department for the whole district The following formula will use local survey data to calculate lawn and garden equipment usage at all public schools Equation 4-5, Equipment to number of schools ratio for public school districts RATIOA = EQA / TOTAL Where, RATIOA = Ratio of equipment type A per public school EQA = Total pieces of equipment type A for public schools that responded to the first survey (from survey data) TOTAL = Total number of public schools that responded to the first survey (from survey data) For school districts that did not respond to the first survey, average equipment population and types will be allocated to each school based on the following formula Equation 4-6, Estimated equipment population by equipment type for public schools that did not respond to the survey SPOPAB = NUMB x RATIOA Where, SPOPAB = Population of equipment type A for school district B NUMB = Number of schools in school district B RATIOA = Ratio of equipment type A per public school (from equation 4-5) FIPS 48013 48019 48029 48091 48187 48259 48325 48493 Table 4-3: Allocation of Public Schools by County, 200936 Total Number of County Percentage Schools* Atascosa 32 5% Bandera 1% Bexar 445 70% Comal 46 7% Guadalupe 40 6% Kendall 15 2% Medina 23 4% Wilson 29 5% Total (San Antonio – New Braunfels MSA) 636 36 100% U.S Department of Education “Search for Public School Districts” National Center for Education Statistics, Washington, DC Available online: http://nces.ed.gov/ccd/districtsearch/ Accessed 10/03/11 4-6 *Military Base Schools are not included (lawn and garden equipment from these schools are included in the Airport/Military emission inventory) 4.3.2 Commercial Lawn and Garden and Land Clearing Companies The methodology proposed to estimate commercial companies’ lawn and garden equipment population and activity rates for the San Antonio MSA relies on local data produced from surveys Also, a 10% adjustment factor will be applied to commercial lawn and garden equipment based on the methodology used by ERG.37 Equation 4-7, Equipment to number of commercial companies ratio RATIOA = EQA / TOTAL Where, RATIOA = Ratio of equipment type A per commercial company EQA = Total pieces of equipment type A for commercial companies that responded to the first survey (from survey data) TOTAL = Total number of commercial companies that responded to the first survey (from survey data) Equation 4-8, Estimated commercial companies’ equipment population by equipment type CPOPAB = NUMB x RATIOA Where, CPOPAB = Population of equipment type A for county B NUMB = Number of commercial companies in county B RATIOA = Ratio of equipment type A per commercial company (from equation 4-7) Table 4-4: Commercial Lawn and Garden Companies in the San Antonio-New Braunfels MSA, 200838 FIPS County Number of Commercial Companies 48013 Atascosa 48019 Bandera 48029 Bexar 235 48091 Comal 44 48187 Guadalupe 25 48259 Kendall 16 48325 Medina 48493 Wilson Total (San Antonio – New Braunfels MSA) 338 4.3.3 Non-Military Government Facilities, Parks, and Hospitals Other facilities with large improved acreage will also be surveyed to determine if they conduct lawn and garden activities These facilities include local municipalities, power generation 37 Rick Baker and Sam Wells, Nov 24, 2003 “Development of Commercial Lawn and Garden Emission Estimations for the state of Texas and Selected Metropolitan Areas” Prepared by Eastern Research Group and Starcrest Consulting Group for Texas Commission on Environmental Quality 38 U.S Census Bureau, Sept 28, 2011 “County Business Patterns” Available online: http://www.census.gov/econ/cbp/index.html Accessed 10/03/11 4-7 companies, hospitals, commercial parks, and state parks If a facility does not respond, a lawn and garden equipment population will not be calculated for this entity 4.4 Conduct a Second Survey of Commercial Lawn and Garden Equipment After analyzing survey results, aerial photographs, district appraisal data, and other data sources, a second survey will be sent out to the local businesses that did not respond to the first survey with the estimations of their equipment population, horsepower, and activity rates The second survey will use the same format as the initial survey Companies and facilities will be asked to correct estimations and send the surveys back to AACOG Once the lawn and garden equipment is tallied for all categories, a comparison will be done between TexN Model existing data and the results from the survey Table 4-5 shows the breakdown by category for AACOG’s 2002 survey results and ERG’s 2002 survey AACOG results match closely with ERG findings for most categories Overall, the TexN Model under predicts the number of lawn and garden equipment in the San Antonio-New Braunfels MSA compared to the results from previous studies AACOG’s 2002 survey of equipment is 310 percent higher than existing data in the TexN Model (ERG results indicate that the number was 223 percent, but they did not survey all categories) There were significantly more rear-engine mowers and shedders in the AACOG survey than indicated by the TexN Model Turf equipment, trimmers, leaf blowers, and chainsaws are also under-predicted in the TexN Model At the same time, the TexN Model over predicted the number of tillers AACOG’s previous survey results for the San Antonio-New Braunfels MSA did not include the lawn and garden equipment at military bases that would increase the percentage of equipment Military equipment was included in the airport and military base emission inventory 4.5 Estimate Ozone Precursors Emissions Once county level equipment population are calculated, VOC and NOX emissions will be calculated using load factors and emission factors from the TexN Model Local population and activity data from the survey will be used in the following formula to calculate ozone season daily emissions Equation 4-9, Ozone season daily emissions for commercial lawn and garden equipment DEA = POPA x OSDA x HPA x LFA x EFA / 907,184.74 grams/ton Where, DEA POP A OSDA HPA LFA EFA = Daily ozone season emissions for equipment type A (tons/day) = Population of equipment type A (from survey) = Ozone season day activity rate for equipment type A, hrs (from survey) = Average rated horsepower for equipment type A, hp (from survey and TexN Model) = Load factor for equipment type A (from TexN Model) = Average emissions factor for equipment type A, g/hp-hr (from TexN Model) 4-8 Table 4-5: Comparison of Pervious Surveys Equipment Population Estimations, NONROAD Model 2008a, and TexN Model Existing Estimates, San Antonio-New Braunfels MSA AACOG 2002 Survey* NONROAD TexN Commercial Equipment Type 2008a Model Commercial Model Existing Lawn and Universities Default Population Garden / Colleges Population (2002) Companies Public Schools Golf Courses ERG Percent of Results for Government Total from TexN Commercial/ San Facilities / Other AACOG's Model Private Antonio Parks / Companies 2002 Population Airports (2002)# Hospitals Survey Lawn Mowers Tillers Chainsaws Trimmers Blowers Rear Mower Front Mower Shredder Tractor Chippers Turf 12,957 4,961 6,218 15,847 9,013 414 3,205 2,601 3,280 835 8,342 2,744 350 2,489 2,734 2,121 995 440 45 406 57 2,894 104 5,099 5,430 4,106 716 4,203 2,221 103 434 72 20 26 320 82 77 61 31 437 47 3,729 348 128 200 197 395 14 35 256 201 536 1,429 90 287 167 547 2,221 492 155 195 16 82 51 24 34 70 15 0 115 17 10 16 22 3,596 123 5,736 12,140 5,261 1,622 6,106 2,479 690 494 419 131% 35% 230% 444% 248% 163% 1,388% 5,514% 0% 122% 737% 231% 292% 107% 232% 347% 205% 186% 0% 0% 201% 359% Other 6,217 1,312 3,607 0 60 43 3,716 283% 227% 187 207 42,383 310% 223% Total 73,890 13,693 28,988 634 5,506 2,848 4,016 *Does not include military Lawn and Garden Equipment # Based on the 2000 4-county MSA: Bexar, Comal, Guadalupe, and Wilson Counties 4-9 4.6 Temporal Allocation A weekday versus weekend adjustment factor will be calculated based on the total hours of commercial lawn and garden equipment use for each time period as determined from the surveys for each equipment type and facility type 4.7 Spatial Distribution Emissions will be allocated to the 4km grid by the location of the golf courses, public schools, universities/colleges, population, and government facilities, parks, and hospitals Golf course locations are provided in figure 4-1, while figure 4-2 shows the locations of universities/colleges in the San Antonio-New Braunfels MSA Locations of public schools are provided in figure 4-3 Figure 4-1: Location of Golf Courses in the San Antonio-New Braunfels MSA Plot Date: October 17, 2011 Compilation Date: October 14, 2011 Source: Aerial Photography, District Appraisal Data, and Telephone Survey 4-10 Figure 4-2: Locations of Universities and Colleges in the San Antonio-New Braunfels MSA Plot Date: October 18, 2011 Compilation Date: October 21, 2011 Source: Aerial Photography 4-11 Figure 4-3: Locations of Public Schools in the San Antonio-New Braunfels MSA Plot Date: October 18, 2011 Compilation Date: October 21, 2011 Source: Aerial Photography and U.S Department of Education 4-12 Agricultural Pesticide Applications The definition of a pesticide from the Title U.S Code 136 (FIFRA) Sec is: (1) “any substance or mixture of substances intended for preventing, destroying, repelling, or mitigating any pest, (2) any substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant, and (3) any nitrogen stabilizer, except that the term “pesticide” shall not include any article that is a “new animal drug” … The term “pesticide” does not include liquid chemical sterilant products (including any sterilant or subordinate disinfectant claims on such products) for use on a critical or semi-critical device.”39 EPA’s Emission Inventory Improvement Program (EIIP) prescribes the preferred methodology for calculating emissions from agricultural pesticide applications This methodology will be used with local data to estimate emissions from the use of agricultural pesticides in the AACOG region, and involves the following steps: Identify the pesticides used in significant volumes in the San Antonio-New Braunfels MSA along with the percentage of active ingredients, vapor pressure of active ingredients, fraction of VOC, application rate for each pesticide, and application time for each crop Calculate emission factors based on vapor pressure of active ingredients, fraction VOC in the formulation, and the application rate of the pesticides Estimate ozone precursor emissions from volatilization of pesticides Emission factors are multiplied by the number of acres of each crop to estimate total emissions of pesticides by crop for each county.40 Spatially allocate emissions based on crop locations Provide raw local input data such as local activity profiles and spatial surrogates to TCEQ Note that pesticides used in the home and gardens are categorized as consumer/commercial solvent use and are not included in this proposal By using default emission factors and usage rates of pesticides, emissions are significantly over estimated When local data on pesticide types, usage, acres by crop type, and application rates are used, emission estimates are significantly improved 5.1 Determine Pesticides Used on Area Crops and Application Timetable Agricultural pesticides are applied to the following harvested crops in the San Antonio-New Braunfels MSA: • Corn • Cotton • Peanuts • Wheat • Sorghum To calculate agricultural pesticide emissions, crop acres planted and harvested for each county will be collected Crop acreages by crop types are available from the 2006 Texas Agricultural Statistics report published by USDA (Table 5-1).41 39 Title 7, Chapter 6, Subchapter II § 136 “Federal Insecticide, Fungicide, and Rodenticide Act: Definitions” p 12 Available online: http://www.epa.gov/opp00001/regulating/fifra.pdf Accessed 10/24/11 40 U.S Environmental Protection Agency, June 2001 “Emissions Inventory Improvement Program: Volume 3, Chapter – Pesticides – Agricultural and Nonagricultural” Research Triangle Park, North Carolina Available http://www.epa.gov/ttnchie1/eiip/techreport/volume03/ Accessed 10/24/11 41 United States Department of Agriculture, Updated December 2009 “Texas Agricultural Statistics, 2006” National Agricultural Statistics Service, Texas Field Office” Available online: 5-1 Table 5-1: Acres Harvested by Crop Type for Each County in the San Antonio-New Braunfels MSA, 2006 FIPS County Corn Sorghum Wheat Cotton Peanuts 48013 Atascosa 0 500 2,200 2,200 48019 Bandera 0 0 48029 Bexar 4,200 2,500 400 2,400 48091 Comal 1,200 0 0 48187 Guadalupe 6,300 11,900 4,200 2,400 48259 Kendall 0 0 48325 Medina 13,100 5,400 3,600 12,000 48493 Wilson 2,600 3,100 600 1,100 Total 27,400 22,900 9,300 20,100 2,200 Types of pesticides commonly used for these crops were obtained from the Bexar County Texas Cooperative Extension.42 The Extension service provided information on pesticides used in the San Antonio-New Braunfels MSA and the times of the year the pesticides are applied (Table 5-2) To determine the composition of each pesticide, the county extension office recommended the Clemson University Cooperative Extension website.43 The website contains a “Pest Management Handbook” that lists and describes pesticides used for each agricultural crops Crop Type Corn Sorghum Peanuts Cotton Wheat Hay Table 5-2: Types of Pesticides Used and Seasonal Adjustment44 Monthly Usage Pesticide After crops are Pegging Spray at heading planted Aztec March N/A N/A Counter 15 March N/A N/A Counter 20 March N/A N/A Karate Z 2.08 CS N/A N/A June-July Lorsban 15 G June/July N/A N/A Temik 15 G May June/July N/A Bidrin EC May/June N/A N/A Orthene 97 PE May/June N/A N/A Temik 15 G June N/A N/A Lorsban 4E March/April N/A N/A none N/A N/A N/A http://www.nass.usda.gov/Statistics_by_State/Texas/Publications/Annual_Statistical_Bulletin/bull2006.pd Accessed 10/06/11 42 August 2004 “The Texas Cooperative Extension, Bexar County office” Available online: http://bexartx.tamu.edu Accessed 10/24/11 43 Clemson University Cooperative Extension 2011 “Pesticide Product Registration” Clemson University Clemson, SC Available online: http://www.clemson.edu/public/regulatory/pesticide_regulation/our_service_areas/pesticide_product_regis tration/index.html Accessed 10/24/11 44 Jerry W Warren Sept 4, 2005 e-mail “Agricultural Pesticides” 5-2 It is important to note that many “Pesticides are applied as a band over the row and not the total area For example, a herbicide may be sprayed in a 10 inch band on rows planted 40 inches apart - so the actual herbicide use would by 10/40 or about 25% of the planted acreage.”45 Once the application rates were calculated, an average application rate will be obtained per pesticide per acre (Table 5-3) Practically none of the 7.2 million acres of small grain crops that are planted in Texas receive pesticide treatments According to Doug Stevenson Ph.D from the Texas Cooperative Extension at Texas A&M University, “Texas plants about 7.2 million acres of wheat every year About 6.9 million are fall grain, and about 300,000 are spring grain About 6.7 million of those acres are wheat All wheat planted in Texas is grazed to some extent According to USDA Ag Statistics Service, only 2.8 to 3.4 million acres are harvested for grain The rest are either grazed off completely or cut for hay Grazing also eliminates problems with the key pests of wheat, such as greenbug (Schizaphis graminum), bird-oat-cherry aphid (Rhopalosiphum padi), corn leaf aphid (Rhopalosiphum maidis), Russian wheat aphid (Diuraphis noxia) and English grain aphid (Macrosiphum avenae) Grazing also eliminates problems from occasional pests, such as chinch bugs (Blissus leucopterus), brown wheat mite (Petrobia latens) and winter grain mite (Penthaleus major), armyworms (Pseudaletia unipuncta, Spodoptera eridania, Spodoptera frugiperda, Spodoptera ornithogalli), and grasshoppers Resistant varieties are used to eliminate Hessian fly and the wheat rusts Seed certification eliminates smuts Grazing replaces virtually all insecticide and fungicide treatments of the crop Texas plants about 2.5 to 3.5 million acres of sorghum About half is grazed and harvested for hay, with less than 10% receiving pesticide applications About 1.5 to million acres are harvested for grain These are grown in rotation with other crops Between half and three-fourths of the grain sorghum requires minimum weed control, usually achievable by cultivation; weed control in the previous crop suppresses most weeds Between 25 and 50 percent of grain sorghum crops receive herbicide treatments Serious insect pests, such as greenbug and sorghum midge are controlled with resistant varieties or cultural practices Late planted fields in South Texas may require pesticide treatment, but these account for only about 5% of the total acres in Texas Taking herbicide and insecticide application figures into account, slightly more than 600,000 acres are treated with pesticide Some estimates of treated crops refer to the large proportion of treated seed planted About 85% of the crops planted in the United States use commercial seed The other 15% comes from farmers who save their seed for replanting Virtually 100% of commercial seed receives treatment by fungicides and about half is treated with additional insecticide The average treatment usually applies about 100 grams of active ingredient per 100 kilograms of treated seed (1g/kg) Since the average seeding rate for most crops is about to 16 kilograms (12.5 to 40 lb) of seed per acre, the amount of any particular seed treatment pesticide entering the environment is about 0.005 to 0.016 kg (0.01 to 0.02 lb) per acre.”46 45 Smith, Dudley, July 26, 2005 e-mail, “Ag practices Pesticides”, Soil and Crop Sciences, Texas A&M University 46 Doug Stevenson, Ph.D Sept 2, 2005 E-mail “Agricultural Pesticides” Extension Associate - Ag Chem, Texas Cooperative Extension, TAMU, College Station, TX 5-3 Crop Corn Sorghum Peanuts Cotton Wheat Hay Table 5-3: Pesticide Application, Usage Rate, and Number of Applications % of Crop that use Number of Pesticide Usage Rate each pesticide Applications Aztec 85% 0.0125 - 0.0400 lbs Counter 15 G 85% 0.0125 - 0.0400 lbs Counter 20 CR 85% 0.0125 - 0.0400 lbs Karate Z 2.08 CS 25% 0.96-1.28 oz/ac Lorsban 15 G 1% 13.3 lbs/ac Temik 15 G 3% 4-7 lbs/ac Bidrin EC 85% 0.0125 - 0.0400 lbs Orthene 97 PE 85% 0.0125 - 0.0400 lbs Temik 15 G 85% 0.0125 - 0.0400 lbs Lordban 4E 0.1% 16 oz/ac None 0% N/A Emission factors per acre will be calculated for both active and inert ingredients by crop type For active ingredients, emission factors per acre is based on pounds of pesticide used per acre multiplied by the fraction of active ingredients and emission factor per pound of active ingredient A similar calculation will be used to calculate inert ingredient emission factors per acre The emissions per acre for active ingredients are added to the emissions per acre for inert ingredients to get a total emission factor by acre for each pesticide Table 5-4 lists the emission factors by crop and pesticide for both active and inert ingredients Emissions for each pesticide will be summed by crop type47 and multiplied by the number of acres for each crop type in each county and the percentage of area the pesticide is applied to each acre to estimate county total emissions 5.2 Calculate Emission Factors for Each Pesticide Below are the equations that will be used to calculate pesticide emission factors for each pesticide type Equation 5-1, Active ingredient emission factor per acre for pesticides AEFA = ARA x FAA x VEFA Where, AEFA ARA FAA VEFA = Active ingredient emission factor per acre for pesticide A = Average application rate for pesticide A, lbs/acre (from Table 5-3) = Fraction of active ingredient for pesticide A (from Table 5-4) = Active ingredient emission factor for pesticide A, lbs/ton (from Table 5-4) 47 Smith, Dudley and Anisco, Juan, August 2000 “Crop Brief on Production, Pests, & Pesticides” Texas A&M AgriLife, Texas A&M University, Texas Agricultural Experiment Station, and the Texas AgriLife Extension Service Available online: http://aggie-horticulture.tamu.edu/extension/cropbriefs/ Accessed 10/24/11 5-4 Table 5-4: Pesticide Use and Emission Factors by Crop Type Active Ingredient Inert Ingredient Total EF Application EF of Inert EF per acre Percentage Active Inert Crop Pesticide Rate (Inert + Active Vapor Active EF of Inert per of Active EF per Formulation (lbs/acre) Ingredient Pressure Ingredient Ingredient acre Active) ingredient acre Type (lbs/ton) (lbs/ton) (lbs/ton) Cyfluthrin 0.1% 2.1E-08 Aztec 3.93 0.35 0.029 Granule/flake 0.25 0.96 0.99 Tebupirimphos 2.0% N/A Corn Counter 15 G 0.09 terbufos 90.0% 3.2E-04 0.58 0.048 Granule/flake 0.25 0.00 0.05 Counter 20 CR 0.09 terbufos 90.0% 3.2E-04 0.58 0.048 Granule/flake 0.25 0.00 0.05 Total for Corn 1.09 lambda Pressurized Karate Z 2.08 CS 0.10 22.8% 1.5E-09 0.35 0.008 0.39 0.03 0.04 cyhalothrin sprays Sorghum Total for Sorghum 0.04 Lorsban 15 G 13.3 chlorpyrifos 15.0% 1.7E-05 0.35 0.029 Granule/flake 0.25 0.12 0.15 Temik 15 G 5.5 aldicarb 15.0% 3.0E-05 0.35 0.029 Granule/flake 0.25 0.12 0.15 Peanuts Total for Peanuts 0.29 Emulsifiable 0.56 0.01 0.06 Bidrin EC 0.10 dicrotophos 82.0% 1.6E-04 0.58 0.048 concentrate 0.09 acephate 97.0% 1.7E-06 0.35 0.029 Pellet/Tablet 0.27 0.00 0.03 Cotton Orthene 97 PE Temik 15 G 0.55 aldicarb 15.0% 3.0E-05 0.35 0.029 Granule/flake 0.25 0.12 0.15 Total for Cotton 0.23 Emulsifiable Lorsban 4E 1.00 chlorpyrifos 44.9% 1.7E-05 0.35 0.16 0.56 0.31 0.47 concentrate Wheat Total for Wheat 0.47 N/A = Not Established 5-5 Equation 5-2, Inert ingredient emission factor for pesticides IEFA = ARA x (1-FAA) x VEFA Where, IEFA ARA FAA VEFA = Inert ingredient emission factor for pesticide A = Average application rate for pesticide A, lbs/acre (from Table 5-3) = Fraction of active ingredient for pesticide A (from Table 5-4) = Inert ingredient emission factor of pesticide A, lbs/ton (from Table 5-4) Equation 5-3, Total emission factor for each pesticide TEFA = AEFA + IEFA Where, TEFA AEFA IEFA = Total emission factor per acre for pesticide A, lbs/acre = Active ingredient emission factor for pesticide A, lbs/acre (from equation 5-1) = Inert ingredient emission factor for pesticide A, lbs/acre (from equation 5-2) 5.3 Estimate Precursor Emissions from Pesticides Once county level pesticide use and emissions factors are calculated, VOC emissions will be calculated Local pesticide use will be used in the following formula to calculate ozone season daily emissions Equation 5-4, Ozone season daily VOC emissions for pesticides use AEAB = ∑ (ACREB x TEFA x NUMA x PERA / 2000 lbs/ton) / 214 ozone season days per year Where, AEA = Ozone season daily VOC emissions for pesticide A for crop type B (tons/day) ACREB = Crop acres for crop type B (from U.S Department of Agriculture (USDA) National Agricultural Statistics Service, Table 5-1) TEFA = Total emission factor per acre for pesticide A, lbs/acre (from equation 5-3) NUMA = Number of applications for pesticide A during the ozone season (from Table 5-2) PERA = Fraction of acres with pesticides A (from Table 5-2) 5.4 Spatial Allocation of Emissions Data from the Natural Agricultural Statistics Service will be used to geo-code pesticide emissions.48 The following crops were identified and will be used to estimate acres in each 4km grid square: • Corn • Peanuts • Wheat • Sorghum • Cotton 48 National Agricultural Statistics Service “CropScape – Cropland Data Layer” United States Department of Agriculture Available online: http://nassgeodata.gmu.edu/CropScape/ Accessed 06/06/11 5-6 Draft maps of crop acreage provided in figure 5-1 will be checked using satellite imagery and data from 2006 Texas Agricultural Statistics reports49 to make sure location of crops were accurate Using the default methodology, agricultural pesticides are evenly distributed over a county or applied to all agricultural acreage By applying the emissions to specific crop type, photochemical model performance can be improved Most crops, including corn, peanuts, wheat, sorghum, and cotton, are grown in the southern and central parts of the San Antonio-New Braunfels MSA These crops are not grown in Bandera and Kendall counties because the soils are not suitable for extensive crop production Once crop locations are identified, agricultural pesticide emissions will be spatially allocated to the 4km photochemical grid system used in the June 2006 photochemical model Future improvements can include averaging crop acreage and production over multiple years because farmers often rotate crops 49 United States Department of Agriculture, Updated December 2009 “Texas Agricultural Statistics, 2006” National Agricultural Statistics Service, Texas Field Office” Available online: http://www.nass.usda.gov/Statistics_by_State/Texas/Publications/Annual_Statistical_Bulletin/bull2006.pd Accessed 10/06/11 5-7 Figu ure 5-1: Acres of Corn, Peanuts s, Wheat, Sorgh hum, and Cotton n for each 4km P Photochemical Modeling Grid Corn Peanuts Sorgh hum Cotton Wheat 5-8 ... Calculate Emission Factors for Each Pesticide 5.3.  Estimate Precursor Emissions from Pesticides 5.4.  Spatial Allocation of Emissions v 4-7   4-8   4-8   4-1 0  4-1 0  5-1   5-1   5-4   5-6   5-6   LIST... Columbia MFG LC4 1-5 50FG Commander Aircraft CO 114 J Church RV-6 Long-EZ Mooney M20R Piper PA-2 3-2 50 Piper PA-2 4-1 80 Piper PA-30 Piper PA-32R-3 Piper PA-3 8-1 12 Rockwell International 500-S Symphony... 4-8   Equation 5-1 , Active ingredient emission factor per acre for pesticides 5-4   Equation 5-2 , Inert ingredient emission factor for pesticides 5-6   Equation 5-3 , Total emission